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p class="3">The potential and use of mobile devices in higher education has been a key issue for educational research and practice since the widespread adoption of these devices. Due to the evolving nature and affordances of mobile technologies, it is an area that requires ongoing investigation. This study aims to identify emerging trends in mobile learning research in higher education in order to provide insights for researchers and educators around research topics and issues for further exploration. This study analysed the research themes, methods, settings, and technologies in mobile learning research in higher education from 2011 to 2015. A total of 233 refereed articles were selected and analysed from peer reviewed journals. The results were compared to three previous literature review-based research studies focused between 2001 and 2010 to identify similarities and differences. Key findings indicated that: (a) mobile learning in higher education is a growing field as evidenced by the increasing variety of research topics, methods, and researchers; (b) the most common research topic continues to be about enabling m-learning applications and systems; and (c) mobile phones continue to be the most widely used devices in mobile learning studies, however, more and more studies work across different devices, rather than focusing on specific devices.</p
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International Review of Research in Open and Distributed Learning
Volume 18, Number 7
November 2017
Research Trends in Mobile Learning in Higher
Education: A Systematic Review of Articles (2011
2015)
Greig Krull and Josep M Duart
Universitat Oberta de Catalunya
Abstract
The potential and use of mobile devices in higher education has been a key issue for educational research
and practice since the widespread adoption of these devices. Due to the evolving nature and affordances
of mobile technologies, it is an area that requires ongoing investigation. This study aims to identify
emerging trends in mobile learning research in higher education in order to provide insights for
researchers and educators around research topics and issues for further exploration. This study analysed
the research themes, methods, settings, and technologies in mobile learning research in higher
education from 2011 to 2015. A total of 233 refereed articles were selected and analysed from peer
reviewed journals. The results were compared to three previous literature review-based research studies
focused between 2001 and 2010 to identify similarities and differences. Key findings indicated that: (a)
mobile learning in higher education is a growing field as evidenced by the increasing variety of research
topics, methods, and researchers; (b) the most common research topic continues to be about enabling
m-learning applications and systems; and (c) mobile phones continue to be the most widely used devices
in mobile learning studies, however, more and more studies work across different devices, rather than
focusing on specific devices.
Keywords: mobile learning, research trends, research methods, pedagogical issues, higher education
Introduction
Many higher education institutions are implementing mobile learning to provide flexibility in learning.
It is expected that this will continue to be a growing trend with the proliferation of wireless devices and
technologies. It is expected that the next generation of mobile learning will be ubiquitous and learners
themselves will be more mobile and able to learn using multiple devices (Ally & Prieto-Blázquez, 2014).
Although there are a number of interpretations of what is meant by mobile learning, this study makes
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use of the definition by O’Malley et al. (2005) as “any sort of learning that happens when the learner is
not at a fixed, predetermined location, or learning that happens when the learner takes advantage of the
learning opportunities offered by mobile technologies. (p. 7).
Mobile devices tend to drive new research opportunities in mobile learning because of the rate of
changes in technologies. In addition to devices, communication technologies have also changed, shifting
the focus of research (Parsons, 2014). For example, social media and messaging apps are
commonplace. The development and usage patterns of mobile technologies in education change quickly.
This means that regular analysis is required of trends in mobile device types and functionality, along
with learner types and the use of mobile devices in various disciplines and courses (Wu et al., 2012, p.
818). The research purposes and methods used in studies are important because they influence how
research results are shared, interpreted and used (Wingkvist & Ericsson, 2011). Review studies can help
to identify progress in the field and offer guidelines for the design of future research (Frohberg, Göth, &
Schwabe, 2009). Understanding the trends in research studies can also help higher education policy
makers in making decisions regarding technology and teaching and learning (Wu et al., 2012).
This paper provides a systematic review of mobile learning research in higher education from 2011 to
2015. It begins with an analysis of previous review studies in order to provide the basis of comparison
with similar studies. The research purpose and questions are then described. The next section discusses
the methodology used to conduct the review study. This is followed by the presentation of the results of
the study, with a comparison to three previous studies. The final section provides a discussion of the
findings of the review study.
Previous Studies
A number of review studies have been conducted in recent years in an attempt to explore and provide
insights into the growing body of knowledge in mobile learning. One of the first reviews in mobile
learning provided an activity-focused perspective of case studies in the use of mobile technologies for
education (Naismith, Lonsdale, Vavoula, & Sharples, 2004). Cheung and Hew (2009) conducted a
review of research methodologies used in mobile learning in school and higher education settings. They
reviewed 44 articles published until the end of 2008 and found that descriptive research was the most
dominant research method and questionnaires were the most used data collection method. Frohberg,
Göth, and Schwabe (2009) conducted a review of 109 mobile learning projects to evaluate and categorise
them against a mobile learning task model. Hwang and Tsai (2011) conducted a study of research trends
in mobile and ubiquitous learning by reviewing 154 articles from six major technology-enhanced
learning journals from 2001 to 2010. They found that the number of studies increased significantly over
the period. They also found that higher education students were the most frequent learning populations
and that most studies did not focus on a specific learning domain. Hung and Zhang (2012) examined
mobile learning trends between 2003 and 2008 by using text-mining techniques to conduct a meta-
trend analysis of 119 articles. They similarly found that studies in mobile learning increased rapidly over
that period. They also found that many studies focused on the effectiveness of mobile learning but there
was increasing focus on evaluation and systems development. Wu et al. (2012) recognised the value of
these two previous studies, but felt further examination was required from the standpoint of research
purposes, methodologies, and outcomes” (p. 817). The authors used a meta-analysis approach to
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3
systematically review 164 mobile learning studies published between 2003 and 2010. They also found
most research purposes focussed on effectiveness and system design, but also found that surveys and
experimental methods were the most used research methods and that the research outcomes in studies
were significantly positive.
Systematic reviews have also been conducted on conference proceedings. Wingkvist and Ericsson (2011)
surveyed 114 papers presented at the World Conference on Mobile Learning (mLearn) conferences in
2005, 2007, and 2008. The focus of the review was on research purposes and research methods. They
found that research methods were evenly distributed, with the exception of basic research (development
of new theories). In terms of research purpose, the majority of papers were descriptive research,
followed by developmental and understanding research. The lack of evaluative research papers was
found to be a problem (Wingkvist & Ericsson, 2011).
A number of review studies have also been conducted to investigate a particular aspect or theme related
to mobile learning. Wong and Looi (2011) conducted a review of mobile-assisted seamless learning
related literature between 2006 and 2011. Baran (2014) studied the literature to “fill the gap” on mobile
learning research in teacher education programmes. Song (2014) investigated methodological issues in
Mobile Computer-Supported Collaborative Learning (mCSCL) research between 2000 and 2014. Liu et
al. (2014) reviewed 63 articles in K-12 education between 2007 and 2012. Hsu and Ching (2015)
reviewed 17 articles to categorise the models and frameworks developed specifically for mobile learning.
Alrasheedi and Capretz (2015) reviewed 19 articles to determine critical success factors affecting mobile
learning.
Parsons (2014) noted the number of previous reviews, yet highlighted that most reviews tended to focus
on a specific subset of the literature or a particular aspect of mobile learning. The purpose of his study
was to provide a full-landscape view of the field of mobile learning” up to and including 2013 (p. 2).
Findings were presented in two forms. A timeline was used to highlight the evolution of mobile learning
through a series of significant “firsts.” Secondly, a mind map was used to summarise the key concerns
in the areas of research, technology, content, learning, and learner (Parsons, 2014).
Research Problem
The number of literature review-based studies and the results of these studies indicate a research field
that is growing and changing. Due to developments in technology, it is worth considering how the field
of mobile learning research is changing and how these studies are applied in higher education
specifically. Although several review studies (Hwang & Tsai, 2011; Wu et al., 2012) have found that the
majority of mobile learning studies take place within higher education, very few mobile learning review
studies have focussed solely on this sector. This study aims to analyse the research topics, methods,
settings, and technologies used in mobile learning research in higher education, published from January
2011 to December 2015. The research questions are:
1. What research methods have been used in mobile learning articles published from 2011 to 2015?
2. What are the research trends in terms of purposes, themes, and technologies?
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3. How do the findings relate to previous mobile learning reviews from 2001 to 2010?
Methodology
A systematic review provides a summary of the research literature, either quantitative or qualitative,
that uses explicit, replicable methods to identify and select relevant studies; and uses objective and
replicable techniques to analyse and summarise those studies (Cooper, 2010, as cited in Bernard,
Borokhovski, & Tamim, 2014). In order to ensure a systematic review process, this study followed the
seven steps suggested by Cooper (2010, as cited in Bernard et al., 2014) for conducting a systematic
review or meta-analysis:
1. Formulate the research problem.
2. Search the literature.
3. Gather information from studies.
4. Evaluate the quality of studies.
5. Analyse and integrate the outcomes of research.
6. Interpret the evidence.
7. Present the results.
These stages are neither mutually exclusive nor entirely distinct; rather, they should be viewed as key
steps in a continuous and iterative process (Cooper, 2010, as cited in Bernard et al., 2014). The first step
in conducting a systematic review is to formulate the research problem, which has been specified in the
section above.
Literature Search
The second step in a systematic review is to search the literature. A limitation may exist in this study,
referred to as publication bias (Bernard et al., 2014), as this study has not surveyed the “grey literature”
such as conference proceedings, technical reports, dissertations, and book chapters. However, the
search was limited to peer reviewed journal articles in order for better comparison between sources and
aligns with the search strategies by Hwang and Tsai (2011), Wu et al. (2012), Baran (2014), and Bozkurt
et al. (2015). Based on these studies, two databases were selected to ensure comprehensive data
collection: Scopus and ISI Web of Science. The starting point involved searching for a combination and
variation of the keywords “mobile learning” or “m-learning and reviewing the results against the
following inclusion criteria:
Must involve mobile learning as a primary condition,
Must focus specifically on learning at the higher education level,
Must be published in a peer reviewed journal between January 2011 and December 2015,
Must be written in English, and
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The full-text of the article must be publically available or available through the researchers’
institutional library subscriptions.
The first database searched was Scopus. A search of the keywords “mobile learning,“m-learning,or
“mlearning” in articles published between 2011 and 2015 resulted in 1024 results. The results were
filtered to remove non-journal sources (955 results remained) and non-English texts (937 results
remained). The researchers then discarded 373 results because they did not have access to the full text.
The remaining 564 results were assessed against the criteria that the primary focus of the article was
mobile learning and within higher education. A total of 348 articles did not meet these criteria, leaving
216 articles to be included in this study.
The second database searched was the ISI Web of Science (SCI/SSCI). A search of the keywords “mobile
learning, “m-learning, or “mlearning” in articles published between 2011 and 2015 resulted in 1703
results. The results were filtered to remove non-journal sources (698 results remained) and non-English
texts (578 results remained). The researchers then discarded 254 results because they did not have
access to the full text. The remaining 324 results were assessed against the criteria that the primary focus
of the article was mobile learning and within higher education. One hundred and sixty-nine articles did
not meet these criteria, leaving 155 articles to be included in this study. These were compared to the
results of the previous database search, and 138 duplicates were excluded and 17 results were added,
resulting in a total of 233 articles to be studied.
Information Gathering
The third and fourth steps in conducting a review are to gather the information from studies and
evaluate the quality of studies. The 233 articles were collected and organised with the bibliographic data
including article title, authors, journal, abstract, keywords, and publication year. Eleven additional
categories related to the articles were coded, based on the studies of Hwang and Tsai (2011), Wu et al.
(2012), Baran (2014), and Bozkurt et al. (2015). The categories were: (a) research purpose, (b) research
theme, (c) conceptual and theoretical background, (d) research method, (e) research design, (f) data
collection method, (g) target population, (h) learning domain/discipline, (i) learning setting, (j) type of
device, and (k) country. Two independent researchers then independently confirmed the coding for the
first six categories. Disagreements between the two coders were resolved through discussion and further
review of the disputed studies by the principal researchers. This review study targeted peer-reviewed
journal articles, which helps to ensure the relative rigour and quality of studies under review (Hsu &
Ching, 2015). The spreadsheet matrix with the 233 categorised articles can be accessed online.
Research Analysis
The fifth step is to analyse and integrate the outcomes of research. This study made use of content
analysis to analyse the data. Content analysis is a method of analysing documents and enables the
researcher to test theoretical issues to enhance understanding of the data (Elo & Kyngäs, 2008). Content
analysis can use a mix of quantitative and qualitative methods so that a combination of bibliometric and
categorical data can be used to reveal trends (Hung & Zhang, 2012). In order to answer our third
research question, the results were then compared to the results of this study with three previous
literature review studies (Hung & Zhang, 2012; Hwang & Tsai, 2011; Wu et al., 2012). It must be noted
that a direct comparison cannot be performed with each aspect of the studies due to differences in the
Research Trends in Mobile Learning in Higher Education: A Systematic Review of Articles (2011 2015)
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approaches, timing, and methods used in this study, but that a useful comparison may still be drawn
between these studies.
Steps 6 and 7 of the systematic content review process are to interpret the evidence and present the
results. The next section of the paper presents the outcomes of this process. Two hundred and thirty-
three articles on mobile learning in higher education published from 2011 to 2015 were included in this
sample: for 2011 22 articles; for 2012 38 articles; for 2013 45 articles; for 2014 68 articles; and
for 2015 60 articles. The frequency of papers is apparent in the sample increase for each year under
study, except for the last.
Journals
These articles were published in 88 different journals. Table 1 shows the frequency of articles from
journals that have three or more articles in this study. Those journals that are open access are denoted
with an OA in brackets after the journal name.
Table 1
Distribution of Journals With Three or More Articles in This Study
Rank
Journals
Frequency
1
Computers & Education
19
2
The International Review of Research in Open and Distributed
Learning (OA)
18
3
Educational Technology & Society (OA)
13
3
International Journal of Interactive Mobile Technologies (OA)
13
5
Computers in Human Behavior
12
5
Turkish Online Journal of Educational Technology (OA)
12
7
British Journal of Educational Technology
11
7
Journal of Universal Computer Science (OA)
11
9
The Turkish Online Journal of Distance Education (OA)
7
10
Australasian Journal of Educational Technology (OA)
5
10
Electronic Journal of e-Learning (OA)
5
10
IEEE Transactions on Learning Technologies
5
13
International Journal of Mobile and Blended Learning
4
13
Research in Learning Technology (OA)
4
15
Journal of Asynchronous Learning Networks
3
15
Nurse Education Today
3
15
Language Learning and Technology (OA)
3
15
The International Journal of Educational Technology in Higher
Education (OA)
3
Countries
This study represented a wide range of developed and developing countries, for a total of 45 countries.
Country categorisation was based on the country where the research was conducted, rather than the
researcher’s affiliation. The countries with the most number of studies represented were United States
(26), United Kingdom (25), Taiwan (21), Spain (16), and Turkey (16). In terms of comparison with
studies from 2001-2010, these findings closely align to the findings of Hwang and Tsai (2011). In their
study, they found that the three countries that contributed the most number of studies were the United
States, Taiwan, and the United Kingdom, which is the same in this study. Hung and Zhang (2012) also
found the top two contributors to be Taiwan and the United States, although South Korea was third in
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their study. As an indication of the expansion of the field of mobile learning, the articles in the study by
Hwang and Tsai (2011) represented studies conducted in 25 countries, while in this study, 45 countries
were represented.
Results
Research Purposes
Each article was categorised according to its research purpose, adapted from the classification presented
by Wu et al. (2012). The original four purposes were: (1) Evaluate Effectiveness, (2) Design a Mobile
System, (3) Investigate the Affective Domain, or (4) Evaluate the Influence of Learner Characteristics.
A similar classification was provided by Hsu and Ching (2015). Two additional categories were added by
the researchers for this study: (5) Develop Theory and (6) Explore Potential, in order to better represent
all possible purposes. These categories were then defined as:
Evaluate the effects: investigates whether mobile devices can improve or enhance student
learning.
Explore the potential: explores how to use a new tool or how a new technology could be used
for learning (usually a small pilot or exploratory study).
Investigate the affective domain: investigates the affective domain includes factors such as
student motivation, beliefs, attitudes, perceptions, and values.
Design a system: designs frameworks or systems where the emphasis is on the development and
presentation of solutions.
Develop theory: create or promote new pedagogical approaches, models, theories, or
frameworks of mobile learning.
Influence of learner characteristics in the learning process: examines the influence of learner
characteristics such as age, gender, ability, experience, learning style, and culture.
As shown in Figure 1, the most common research purpose was found to be to evaluating effectiveness
(24%), followed by designing a mobile system (23%), and investigating the affective domain (19%). In
terms of comparison with 2001-2010, these findings are similar to those of Wu et al. (2012) in that
evaluating effectiveness was the most common method, followed by designing a mobile system.
However, studies investigating the affective domain, previously a very small research purpose in terms
of the number of studies, have become a greater point of focus.
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Figure 1. Distribution of studies by research purpose.
Themes
It is difficult to find a common list of themes within mobile learning as the categorisation of mobile
learning research depends on the focus of the interests of the researchers (Parsons, 2014). For example,
researchers such as Parsons (2014) and Hsu, Ching, & Snelson (2014) have provided different
categorisations. In this study, the researchers decided to adapt the themes proposed by the annual
International Conference on Mobile Learning Conference themes (http://mlearning-conf.org/). Figure
2 shows the distribution of research themes in studies from 2011-2015. Although several articles
contained overlapping themes, each article was categorised into one major theme for the purpose of this
review. Studies covered a wide range of themes within mobile learning in higher education. The most
common research theme focused on enabling m-Learning applications and systems (23%), followed by
socio-cultural context and implications of m-Learning (13%), and tools and technologies for m-Learning
(12%). No comparison can be done with the research studies from 2001-2010 as the research themes as
categorised in this study were not within the scope of the studies of Hwang and Tsai (2011), Hung and
Zhang (2012), and Wu et al. (2012).
Research Trends in Mobile Learning in Higher Education: A Systematic Review of Articles (2011 2015)
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Figure 2. Distribution of studies by research theme.
Researchers continue to investigate a wide variety of research themes or topics. The most common
research theme for mobile learning in higher education is the wide variety of applications and systems
that are used to enable learning. Existing systems such as text messaging can be used to communicate
with or support students (Lim, Fadzil, & Mansor, 2011) or custom applications can be designed for
specific subjects (Wu, 2015). The next most common theme is the exploration and use of new tools and
technologies for mobile learning. These include specific devices such as smartphones (Gikas & Grant,
2013), tablets (Churchill & Wang, 2014; Engin & Donanci, 2015) and other devices. Researchers are also
interested in the social and cultural contexts that surround mobile learning (Arpaci, 2015; Viberg &
Gronlund, 2013). Educators are exploring how to use social media such as Twitter (Hsu & Ching, 2012)
for learning. Researchers are also developing pedagogical approaches or theories for mobile learning
(Dennen & Hao, 2014; Park, 2011). Other researchers have provided strategies for integrating mobile
learning and overcoming challenges to mobile learning implementation (Brown & Mbati, 2015;
Cochrane, 2014). A few studies have also examined differences in learners and faculty by studying users
within mobile learning (Mac Callum, Jeffrey, & Kinshuk, 2013; Lin, Zimmer, & Lee, 2013). Educators
are also interested in learning within classes and out of classes. In-class systems may include student
response systems (Calma, Webster, Petry, & Pesina, 2014), while researchers are also interested in
informal learning outside of classrooms (Reychav, Kobayashi, & Dunaway, 2015). Innovative learning
approaches include a variety of different approaches. Studies have used context-aware mobile learning
services to personalise learning (Lu, Chang, Kinshuk, Huang, & Ching-Wen, 2014; Wu, Hwang, Su, &
Huang, 2012) or made use of mobile augmented reality (Fonseca, Martí, Redondo, Navarro, & Sánchez,
2014). The use of gamification has been used to promote motivation (Bartel & Hagel, 2014). Learner
mobility studies have focused on learners using devices for collaborative learning in the field (Redondo,
Fonseca, Sánchez, & Navarro, 2014). Another area of interest for educators and researchers is the use of
assessment and evaluation. For example, integrating the use of mobile quizzes into learning processes
(Bogdanović, Barać, Jovanić, Popović, & Radenković, 2014). Researchers have focused on integrating
cloud computing into mobile learning (Wang, Chen, & Khan, 2014). Researchers have also investigated
how mobile learning is researched and implemented, through review studies (Baran, 2014). With
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increasing amounts of data available, educators are interested in using learning analytics to understand
and optimise learning processes and environments (Tabuenca, Kalz, Drachsler, & Specht, 2015).
Theoretical and Conceptual Backgrounds
Every research study should have clear theoretical or conceptual backgrounds (Bozkurt et al., 2015). In
classifying the theoretical and conceptual backgrounds specified in research articles, insights may be
provided regarding the kinds of topics and how researchers are approaching them in mobile learning in
higher education. Where stated, the theories and or concepts stated in the articles were included in the
categorisation, adapted from the classification by Bozkurt et al. (2015). Table 2 lists the most frequently
stated theories or underlying concepts. In several articles, multiple theoretical or conceptual
backgrounds were used together; however, Table 2 only highlights the frequency of the theoretical or
conceptual backgrounds in the population.
The most frequently stated theoretical or conceptual backgrounds model how users come to accept and
use a new technology (Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of
Technology (UTAUT), and Diffusion of Innovation). A strong emphasis can be seen on collaboration
within a community (Collaborative Learning, Communities of Practice). Another trend can be seen to
be moving to a learner-oriented paradigm focussing on student experiences in a social world (Activity
Theory, Social Constructivism, Constructivism) and authentic learning experiences (Authentic
Learning). With the affordances of mobile technologies leading to educators redesigning their curricula
or modes of provision, instructional design theories are also important (Cognitive Load Theory,
Instructional Design).
Table 2
Distribution of Most Common Theoretical or Conceptual Backgrounds
Theoretical / Conceptual background
Frequency
Technology Acceptance Model (TAM)
23
Unified Theory of Acceptance and Use of Technology
(UTAUT)
9
Collaborative Learning
6
Activity Theory / Systems
5
Cognitive Load Theory
5
Diffusion of Innovation
5
Self-regulated / Self-managed Learning
5
Authentic Learning
4
Communities of Practice
4
Learning Styles
4
Scaffolded Learning
4
Social Constructivism
4
Socio-cultural Theory
4
Technological Pedagogical And Content Knowledge
(TPACK)
4
Adaptive Learning
3
Constructivism
3
Cultural Dimensions
3
Instructional Design
3
Substitution Augmentation Modification Redefinition
(SAMR)
3
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No comparison can be done with the research studies from 2001-2010 as the theoretical and conceptual
background was not a specific focus of the studies of Hwang and Tsai (2011), Hung and Zhang (2012),
and Wu et al. (2012).
Research Designs
A mobile learning study generally employs a quantitative, qualitative, or mixed research method like
other educational fields (Bozkurt et al., 2015). In this review, researchers in mobile learning in higher
education mostly conducted qualitative research (46%) or quantitative research (43%), with fewer
studies employing mixed methods (11%). No comparison can be done with the research studies from
2001-2010 as the research method was not a specific focus of the studies of Hwang and Tsai (2011),
Hung and Zhang (2012), and Wu et al. (2012).
In addition to the research method, the research design can also be explored within each of the methods.
The methods used to categorise the research were adapted from Bozkurt et al. (2015) and Creswell
(2009). Table 3 indicates that the most commonly used research designs for quantitative studies were
descriptive surveys (17%), followed by correlational studies (13%), and experiments (12%). Table 3 also
indicates that the most commonly used research design for qualitative studies was design-based
research (18%), followed by case studies (17%), and action research (3%). For mixed methods, the most
common research designs used were sequential explanatory (7%), concurrent triangulation (3%), and
sequential exploratory (1%).
Table 3
Distribution of Studies by Research Design
Quantitative (43%)
Qualitative (47%)
Mixed (11%)
Case Study
1%
Action Research
3%
Concurrent
Triangulation
3%
Correlational
13%
Case Study
17%
Sequential
Explanatory
7%
Experiment
12%
Content Analysis
3%
Sequential
Exploratory
1%
Survey
17%
Design-based
18%
Grounded Theory
1%
Meta-synthesis
2%
In terms of comparison with 2001-2010, the quantitative method findings closely align to the findings
of Wu et al. (2012). They found the most common methods for quantitative studies to be experiments
and descriptive research. However, the qualitative methods are different in that Wu et al. (2012) did not
find case studies, action research, nor other qualitative methods to be widely used. A caution must be
noted though that Wu et al. (2012) presented their results with a different classification and integrated
the presentation of results for both research methods and data collection methods.
Data Collection
Data collection methods were also investigated in this study. Methods were coded into seven categories,
adapted from Song (2014) and Cheung and Hew (2009). Table 4 shows that the most common method
used was a survey (47%) followed by interviews/focus groups (18%) and assessments (13%). Studies
utilised between one and five data collection methods, with 57% of studies utilising one method and 28%
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of studies utilising two methods. Twelve percent of studies utilised three methods, while 3% utilised four
methods.
Table 4
Distribution of Studies by Data Collection Method
Method
Instruments or techniques
Frequency
Assessment
Tests or quizzes
13%
Document Review
Examination of documents
5%
Interviews/Focus
Groups
Discussions between researchers, staff, or students
18%
Observation
Visual examination and documenting actions and utterances of
participants, either directly or via recording
3%
Process Data
Estimates of time, frequency and sequence as well as tracing
data and learning analytics obtained from systems and devices
6%
Product Data
All outputs produced by participant activities such as course
assignments
7%
Survey
Questionnaires, surveys, and scales
47%
In comparison with studies from 2001-2010, the collection method findings do align somewhat to the
findings of Wu et al. (2012) in that surveys continue to be the most common format of collecting data.
However, the current study results seem to indicate that a wider range of data collection methods were
used (2011-2015) than previously.
Population Groups
It was found that the vast majority of studies were aimed at students (78%). A few studies focused on
faculty (10%) or a combination of both faculty and students (12%). Of the studies that focused on
students, 75 studies distinguished between undergraduate and postgraduate levels of students. Of these
studies, 81% studies focussed on undergraduates and 19% focused on postgraduate students. As both
faculty and student adoption play a part in the success of mobile learning initiatives, it is recommended
that more studies in the future look to investigate the implications for both faculty and students. A major
difference between this study and previous studies by Hwang and Tsai (2011) and Wu et al. (2012) is
that this study only focused on the higher education sector. However, both Hwang and Tsai (2011) and
Wu et al. (2012) similarly found that the majority of mobile learning studies across all sectors focused
on higher education students.
Academic Disciplines
Wu et al. (2012) define an academic discipline as a branch of knowledge that is taught or researched at
the higher education level. This study follows the discipline taxonomy used by Wu et al. (2012) who
adopted it from the taxonomy developed by Becher (1994), Wanner, Lewis, and Gregorio (1981), and
others. This taxonomy identifies five major categories of academic discipline: humanities, social
sciences, natural sciences, formal sciences, and professions and applied sciences. Academic subjects
listed in the Classification of Instructional Programs (CIP) (Institute of Education Sciences, 2010) can
be classified within these disciplines. These disciplines and subjects are listed in Table 5. A third (33%)
of mobile learning studies in higher education are across disciplines (generic) or not discipline-specific.
If the remaining studies are classified according the above taxonomy, the most frequent are professions
and applied sciences (34%), followed by humanities (16%), formal sciences (11%), social sciences (3%),
Research Trends in Mobile Learning in Higher Education: A Systematic Review of Articles (2011 2015)
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and natural sciences (3%). In terms of individual sub-disciplines, languages and linguistics was the most
common focus (35 studies), followed by education (28 studies), computer science (26 studies), and
health sciences (26 studies).
Table 5
Distribution of Disciplines and Sub-disciplines
Discipline
Subject
Number of
studies
1. Humanities (16%)
1.1 History
0
1.2 Languages and Linguistics
35
1.3 Literature
0
1.4 Performing Arts
0
1.5 Philosophy
0
1.6 Religion
1
1.7 Visual Arts
3
2. Social Sciences
(3%)
2.1 Anthropology
0
2.2 Archaeology
0
2.3 Area Studies
0
2.4 Cultural & Ethnic Studies
1
2.5 Economics
0
2.6 Gender & Sexuality Studies
0
2.7 Geography
3
2.8 Political Science
0
2.9 Psychology
2
2.10 Sociology
2
3. Natural Sciences
(3%)
3.1 Space Sciences
1
3.2 Earth Sciences
2
3.3 Life Sciences
1
3.4 Chemistry
2
3.5 Physics
0
4. Formal Sciences
(11%)
4.1 Computer Science
26
4.2 Logic
0
4.3 Mathematics
2
4.4 Statistics
0
4.5 Systems Science
0
5. Professions /
Applied Sciences
(34%)
5.1 Agriculture
0
5.2 Architecture & Design
5
5.3 Business
12
5.4 Divinity
0
5.5 Education
28
5.6 Engineering
8
5.7 Environmental Studies and Forestry
1
5.8 Family and Consumer Science
0
5.9 Health Sciences
26
5.10 Human Physical Performance and Recreation
0
5.11Journalism, Media Studies and Communication
1
5.12 Law
0
5.13 Library and Museum Studies
2
5.14 Military Science
0
5.15 Public Administration
0
5.16 Social Work
0
5.17 Transportation
0
Generic (Across
Disciplines) (30%)
Generic (Across Disciplines)
81
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In terms of comparison with studies from 2001-2010, these findings closely align to the studies by
Hwang and Tsai (2011) and Wu et al. (2012). Wu et al. (2012) found that the most common disciplines
to be professions and applied sciences (29%), humanities (20%), and formal sciences (16%). Similar to
findings by Hwang and Tsai (2011), a significant proportion of studies do not focus on a specific
discipline, but are generic or across disciplines. Thus, it can be seen that mobile learning continues to
be applied across most disciplines and that researchers from different disciplines can collaborate. In
terms of sub-disciplines or subjects, the present study has similar findings that languages and
linguistics, computer science, and health sciences are well represented. Language and health science
educators seem to be more eager to adopt the affordances of mobile learning, where practical benefits
can be seen for students. Mobile-assisted language learning (MALL) is a particularly growing area
(Viberg & Gronlund, 2013; Wu, 2015). The present study shows that the education discipline has become
more of a focus for researchers. It is theorised that educators in computer science and education may be
more prone to take advantage of technological innovations in learning. Nonetheless, more studies are
required that show how mobile learning is adopted in other academic subjects. For future research at a
category level, it is recommended that more research studies be conducted in the natural and social
sciences.
Research Settings
Figure 3 shows the distribution of research settings. The categories of research settings were adapted
from Song (2014) and Zheng, Huang, & Yu (2014). Most often, research was carried out in both in class
and out of class settings (33%), followed by research carried out in class settings (16%) and research
conducted across settings (15%). Research also took place in field settings, out of class settings, and in
distance settings. More studies are needed in the future that focus on learner mobility and transitions
across different settings.
Figure 3. Distribution of studies by research setting.
No comparison can be done with the research studies from 2001-2010 as research settings were not a
specific focus of the studies of Hwang and Tsai (2011), Hung and Zhang (2012), and Wu et al. (2012).
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Devices
Figure 4 shows the distribution of mobile devices used in the studies from 2011 to 2015. As indicated,
the majority of studies (107) studied non-specific / generic mobile devices or learning across mobile
devices. This may indicate that as technology changes so quickly, it may be best not to invest in a specific
device as mobile learning can take place across a multitude of devices. This result may also be indicative
of the growing realisation of Bring-Your-Own-Device (BYOD) (Cochrane, Antonczak, Keegan, &
Narayan, 2014; Traxler, 2016). If one looks at the specific device trends, it is clear that mobile phones
(including smartphones) are the most frequently used devices in studies (73). It must be noted that 38
of the 73 studies using mobile phones specified the use of smartphones in particular. Tablets are also
very frequently used in studies (33). For those studies that reported the specific brand of tablet, the
Apple iPad was the overwhelmingly most used tablet brand.
Figure 4. Distribution of devices by year.
In terms of comparison with studies from 2001-2010, the results demonstrate the changes in available
technologies since the study conducted by Wu et al. (2012). However, mobile phones are still the most
common devices used in studies. An increasing number of studies have focused on the use and
affordances of smartphones (for example, the use of specific apps) rather than basic phones and features
(for example, text messaging). Changes in available devices and emerging technologies influence the
studies that are conducted. For example, previous studies made significant mention of PDA devices,
whereas in the more recent studies from 2011-2013, these are seldom mentioned, and not mentioned at
all in 2014-2015 studies. Tablet devices, particularly the Apple iPad, launched in 2010, have become
much more prevalent.
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Discussion
The results of this study reveal research trends and issues in mobile learning in higher education. Mobile
learning continues to be a growing area of research in higher education as evidenced by the number of
academic articles published between 2011 and 2015 and the number of countries where this research
was conducted. Forty-five countries were represented in this study. The results of this study have several
implications for future research in mobile learning in higher education.
Need for Expansion of Focus of Research Themes
The most common research purpose was found to be evaluating the effectiveness of mobile learning
(24%), followed by the design of a mobile system for learning (23%). This study found that the three
most common research themes together (mobile applications and systems; socio-cultural contexts; and
tools and technologies) account for almost half of the mobile learning studies in higher education (48%).
Figure 5 shows the research themes according to research purpose. This figure shows that there are
several themes that are underrepresented in current studies. Consideration of those themes that have
fewer studies should lead to researcher reflection and more studies in those areas to lead to a more
complete understanding of the field. As a growing research field, the themes within mobile learning in
higher education will change over time. However, several themes merit specific attention. More research
and practice is required in themes related to innovative approaches (such as context-awareness services,
augmented reality, and gamification). Additionally, studies that focus on learner mobility and
transitions across different settings are areas where more research is needed. Finally, the use of newer
technologies such as cloud computing and learning analytics may become greater themes of focus for
researchers.
Research Trends in Mobile Learning in Higher Education: A Systematic Review of Articles (2011 2015)
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Figure 5. Research themes and purposes radar chart.
Promotion of Variety in Research Design
In terms of research methodology, both qualitative (46%) and quantitative (43%) approaches were used
most often, with the remainder of studies utilizing a mixed methods approach. A variety of research
designs were employed by researchers; the most common data collection methods were surveys (47%),
interviews/focus groups (18%), and assessments (13%). These findings align closely with studies from
2001-2010, but it appears that a wider variety of methods are increasingly being utilised. For future
studies, it is recommended that authors are clear in describing the methodology used in their studies
and include the theoretical/conceptual background, research design, data collection methods, data
analysis approach, population groups, academic discipline, and research setting. Due to the various
research topics and approaches in this expanding research field, there is a need for a wide range of
research designs. However, the authors would like to point out that more studies in the future should
look to make use of mixed methods research approaches. These approaches can combine the strengths
of quantitative and qualitative methodologies. It is further recommended that more longitudinal studies
are required, as well as studies across more than one individual course in order to understand the long-
term effects and impact of mobile learning initiatives. This will also assist with understanding issues
around sustainability and scale. Fewer studies are required that compare the mode of teaching and
learning (mobile learning or e-learning). This is because of the many variable conditions within a mode
of teaching and learning. Researcher attempts to keep all other conditions the same, can lead to a
suppression of the conditions that may flourish in a particular mode (Bates & Sangra, 2011).
Growth of Bring-Your-Own-Device (BYOD) and Multiple Devices
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A key finding from the study was that a significant proportion of studies did not focus on a specific device
for learning, and instead focused on a generic device or on multiple devices. For studies where a device
was specified, mobile phones (including smartphones) were the devices most commonly used in studies,
followed by tablets. Increasingly, educators and researchers cannot rely on funding for studies where
students or staff are provided with specific devices for learning. Further studies are required that look
at the personal devices that students have access to and how they access content and university services
from these devices. However, BYOD goes beyond access to devices as students are no longer limited to
institutional systems, but increasingly have their own internet access and make use of their own services.
Devices are important, but the associated systems and networks are equally significant (Traxler, 2016).
Access and use of these devices by a majority of students presents challenges and opportunities for the
support and provision of learning (Traxler, 2010). Further research is required in how BYOD strategies
are incorporated into university teaching and learning and the provision of associated academic and
technological support. For the successful integration of mobile learning, faculty need to critically assess
the use of mobile devices for learning and design specific learning experiences that take advantage of
the affordances of mobile devices. Otherwise, mobile learning may continue to be restricted to viewing
a mobile version of an institutional learning management system. Very often, students have access to
more than one personal device. Students may use of multiple devices and these devices can change over
time. New technologies arrive all the time, enabling faculty and students to explore new ways to learn
with these tools (Parsons, 2014). For example, future studies may focus on the impact of wearable
technologies in learning.
Focus on Sustainability and Mainstreaming of Mobile Learning
Increasingly, advanced mobile technologies have become integrated into society, but despite the
potential, have not yet been “fully and formally integrated into higher education” (Traxler, 2016,
“Looking backward”, para. 3). Many innovative research projects in mobile learning in the last 15 years
did not extend beyond pilot projects to become embedded or mainstreamed in education, in part
because of financial and cultural barriers (Traxler, 2016). Further research into how mobile learning
studies can be scaled up or embedded into higher education institutions would be useful. It is expected
that in the next 10 years, mobile technologies will continue to become more popular, personal, and
social. This means that mobile and connected learners can potentially change the nature of teaching and
learning. With the aid of mobile technologies, students can easily “generate, store, share, discuss and
consume images, ideas, information and opinions, can access the cloud, and the services it provides, and
can access each other” (Traxler, 2016, “Looking forward,” para. 8). Often this takes place outside of
institutional systems and applications. This has profound implications for how faculty design courses
and facilitate learning.
Conclusion
Similar to previous review studies, this research aims to provide analysis and guidance for the selection
of research topics and methods within mobile learning (Hung & Zhang, 2012). Systematic reviews can
generate suggestions and insightful implications for researchers and educators aiming to provide
meaningful mobile learning experiences and environments (Hsu & Ching, 2015). The reviews of Hwang
and Tsai (2011), Hung and Zhang (2012), and Wu et al. (2012) applied to research studies from 2001
until 2010. This study examined articles from 2011 to 2015 as follow up research to consider the
Research Trends in Mobile Learning in Higher Education: A Systematic Review of Articles (2011 2015)
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similarities and differences in an expanding field. This research focused solely on the higher education
context. Following a search of three academic databases, 233 peer-reviewed articles were selected and
organised for review. The researchers used content analysis to analyse the data around categories related
to research purpose, theme, method, target population, setting, device, and others. In comparison with
previous reviews, similarities were found with regard to research purposes and research methods used.
Key findings indicate that researchers conduct studies in mobile learning in higher education for a
variety of reasons, but that evaluating the effectiveness is the most common purpose. Similarly, a variety
of themes within mobile learning are explored, but the most common topic focuses on enabling
applications and systems. An increasing number of studies have focused on the use and affordances of
smartphones (for example, the use of specific apps) rather than basic phones and features (for example,
text messaging). Newer research topics relate to mobile learning and social networking, games and
augmented reality. Research methods are split between quantitative and qualitative methods. Data
collection continues to focus primarily on surveys, but a wider variety of methods is being utilised. A
significant proportion of studies do not focus on a specific mobile device, but across devices in mobile
learning. The research shows the increasing trend of BYOD. Mobile phones are still the most common
devices used in mobile learning studies (including smartphones), but tablets are increasingly popular. A
significant change is occurring through BYOD, where learning with multiple personal devices is possible.
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... Mobile learning, also known as m-learning, refers to the process of acquiring knowledge and skills using mobile devices such as smartphones and tablets (Krull & Duart, 2017;Sung et al., 2016). It is flexible learning that can take place anytime and anywhere, allowing learners to take advantage of the opportunities offered by these devices in a variety of contexts (Grant, 2019;Krull & Duart, 2017;Sung et al., 2016). ...
... Mobile learning, also known as m-learning, refers to the process of acquiring knowledge and skills using mobile devices such as smartphones and tablets (Krull & Duart, 2017;Sung et al., 2016). It is flexible learning that can take place anytime and anywhere, allowing learners to take advantage of the opportunities offered by these devices in a variety of contexts (Grant, 2019;Krull & Duart, 2017;Sung et al., 2016). Thanks to the properties and functionalities of mobile devices, mobile learning has the potential to expand pedagogical and didactic boundaries and connect different learning locations and contexts that were previously separated (Grant, 2019;Krull & Duart, 2017;Sung et al., 2016). ...
... It is flexible learning that can take place anytime and anywhere, allowing learners to take advantage of the opportunities offered by these devices in a variety of contexts (Grant, 2019;Krull & Duart, 2017;Sung et al., 2016). Thanks to the properties and functionalities of mobile devices, mobile learning has the potential to expand pedagogical and didactic boundaries and connect different learning locations and contexts that were previously separated (Grant, 2019;Krull & Duart, 2017;Sung et al., 2016). ...
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Despite the importance of physics practical work in higher education, its implementation is often hampered by various constraints and problems. Technology, such as learning management systems (LMS) and mobile learning, can offer solutions to some of these problems and enrich students' learning experiences. Therefore, this research proposes a model called Practical Works in Physics via Mobile Learning and LMS (PWP-MLMS) that exploits features of LMSs and mobile devices to overcome specific challenges encountered in physics practical works and improve students' performance in these works. The model was designed, validated, and evaluated within the teaching context of a Moroccan university. To assess the model's effectiveness,128 students in the Bachelor of Education, Physics-Chemistry specialization were randomly divided into two groups of 64 students each: an experimental group using the model for practical work on the topic of rectification and filtering in the electronics module, and a control group following the conventional method for the same practical work. The results of the evaluation showed that the proposed model can significantly reduce the time required to complete the practical work, have a positive influence on the students' technical skills, and improve the quality of their laboratory reports. Keywords: mobile learning, LMS, practical work, physics education, higher education
... Multiple mobile devices such as smartphones, iPads, iPods, and PDAs have made their way into education due to the potential of mobile learning environments to transcend time and space constraints while providing access to a vast array of educational resources (Andujar, 2016). Within the realm of education, language learning has emerged as a particularly notable area of study to investigate the influence of m-learning (Krull and Duart, 2017). As the exploration of the impact of ...
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The rapid proliferation of mobile technology and its widespread integration in education, particularly in language instruction and acquisition, as well as its effectiveness in facilitating collaborative learning, have recently sparked a surge in research focused on collaborative mobile-assisted language learning (C-MALL). This review sought to delineate the current landscape of literature on C-MALL practices, pinpoint research trends, and propose avenues for future research while providing valuable insights for C-MALL pedagogical strategies. To achieve this objective, the review adhered to the PRISMA protocol, analyzing 72 studies sourced from five databases following stringent inclusion and exclusion criteria. Key findings encompassed: (1) an escalating yearly publication trend on C-MALL practices, with a substantial spike observed post-2019; (2) a dominant contribution from Asian nations in terms of publication volume; (3) a prevalence of studies conducted in higher education settings employing mixed-method or quantitative methodologies on small sample sizes over short durations; (4) C-MALL designs predominantly leveraging social media collaborative learning platforms via smartphones; and (5) the most prominent keywords being motivation, writing, and engagement. The implications of these findings for both researchers and educational practitioners were thoroughly deliberated upon as per the review outcomes.
... Recognizing and managing cognitive load in the m-learning context is vital for designing effective instructional strategies and optimizing learning performance (Clark & Mayer, 2011;Krull & Duart, 2017). M-learning environments engage students with multimedia elements, presenting both realworld and digital screens (Chu, 2014;G. ...
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Aim/Purpose: This study aims to analyze the cognitive load experienced by secondary school students in Biology within m-learning environments and its impact on learning performance. Background: Cognitive load has become a critical issue that schools need to address to ensure students can excel in their learning without being overwhelmed. While principles for reducing cognitive load have been extensively discussed in previous research, studies focusing on mobile learning (m-learning) for Biology among students in Malaysia remain limited. This study employed Cognitive Load Theory (CLT) and Cognitive Theory of Multimedia Learning (CTML) to address this gap. By integrating four key principles—segmenting and pretraining, modality, redundancy, and seductive details—into m-learning tasks using the Successive Approximation Model (SAM1), this study aimed to reduce cognitive load and enhance students’ learning performance. Methodology: This study employed a quantitative approach using a randomized pre-test/post-test quasi-experimental design. Students were randomly assigned to either an intervention group (20 students) or a control group (18 students). The study was conducted over four weeks, comprising a three-week intervention period with a one-week interval. Statistical analyses, including independent t-tests, Mann-Whitney U tests, Quade ANCOVA, and Pearson correlation, were used to analyze the quantitative data. Qualitative feedback was analyzed using thematic analysis. Contribution: This study contributes by providing instructional design strategies that incorporate principles for reducing cognitive load in mobile learning for Biology. It also demonstrates how Cognitive Load Theory (CLT) and Cognitive Theory of Multimedia Learning (CTML) can be effectively integrated. By examining the cognitive load experienced by secondary school students in m-learning environments, the study offers valuable insights for designing and implementing effective instructional strategies. Identifying the factors influencing cognitive load enables educators to develop targeted interventions that enhance learning experiences and optimize performance. Findings: The study indicated that the adoption of mobile learning tasks not only significantly reduced cognitive load but also corresponded to enhanced learning performance. Participants engaging in m-learning experienced lower cognitive load, which was positively associated with superior performance in learning tasks, emphasizing the beneficial impact of mobile learning on cognitive load management and academic achievement. Recommendations for Practitioners: Educators and instructional designers are encouraged to incorporate cognitive load principles into their instructional strategies and learning material design to enhance student performance. Policymakers should consider similar strategies to reduce the cognitive load for students in educational settings to improve learning outcomes. Recommendation for Researchers: Researchers are encouraged to replicate the design elements used in this study when developing mobile or online learning materials to reduce learners’ cognitive load and enhance their performance. They should also consider expanding this research to other topics, subjects, and educational levels to provide further insights and validate the effectiveness of these design elements across different contexts. Impact on Society: The findings of this study have significant implications for society, particularly in addressing mental health and stress issues among the younger generation. By identifying strategies to manage cognitive load and reduce stress in online learning environments, the study provides valuable insights for educators, parents, and policymakers. These strategies can help mitigate the adverse effects of cognitive overload, improve learning experiences, and promote better mental well-being. Additionally, the study’s recommendations can guide the development of more effective and supportive learning environments, contributing to overall societal well-being and academic success. Future Research: Future studies could explore cognitive load beyond the intrinsic and extraneous components focused on in this study, examining additional elements within the frameworks of cognitive load theory and multimedia learning. In addition to using the cognitive load questionnaire, exploring other measurement tools could ensure a more comprehensive understanding of cognitive load. Future research might also consider enriching mobile learning tasks by diversifying subject matter and conducting longitudinal cohort studies. Such studies could provide valuable insights into memory retention over extended periods, aiding in optimizing mobile learning frameworks and enhancing educational experiences.
... For instance, texting, "applications," and social media are widely used. [1] Mobile technology is evolving swiftly, and so is how it is used in education. As a result, it is necessary to regularly analyze "trends in mobile phone types and performance, along with the types of learners and their utilization of handheld devices in different fields and courses." ...
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Students in higher education are using mobile devices, which challenges our understanding of what it means to learn in modern environments. By using mobile devices as instructional aids, students gain a variety of social, cognitive, and technical abilities. Mobile devices may therefore be seen as supporting the growth of employability skills and broader lifelong learning, both of which may be helpful in a variety of circumstances. The quickly evolving landscape of technology and education presented teachers with a number of challenges, and they had to retrain and refresh their skills in order to provide practical training. As a result, heutagogical methods are ideal for enhancing their collective knowledge and skills. Online and blended learning (BL) offer a learning environment that incorporates technological affordances to facilitate learning. This enables the growth of an independent, capable, and self-directed lifelong learner. Heutagogy provides an educational strategy in this context that may link the development of lifelong learning competencies with BL and online learning environments. Using this methodology, we investigate the potential contribution of online and blended higher education to technology-enabled lifelong learning where heutagogical experiences are available. The results support the notion that heutagogy and lifelong learning are linked by some basic concepts that apply to both mixed and online learning environments.
... According to research, combining social learning platforms and collaborative technology can allow students to engage, share ideas, and learn from one another in natural and virtual environments. According to Krull and Duart (2017), mobile learning is a notable trend because of its flexibility and ease. ...
... The most well-established model for measuring improvement in learning outcomes is the technology acceptance model, which can predict behavioral intentions in using technology (Sprenger & Schwaninger, 2021). The interactive learning process is supported by technology-based learning tools (Krull & Duart, 2017). It is expected that learning in the classroom and outside the classroom is always optimal with the presence of mobile learning (H. ...
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Mobile learning is highly recommended for teachers in implementing virtual learning during the COVID-19 pandemic as a substitute for face-to-face learning. This study aims to provide an overview of the development of Augmented Reality (AR)-based Learning Mediums in improving students' spatial intelligence on the basics of Mapping, Remote Sensing, and Geographic Information Systems in SMA in West Sumatra. The results showed that there was an increase in the spatial ability of students in the Basics of Mapping, Remote Sensing, and Geographic Information Systems in Class X's odd semester using augmented reality-based mobile learning.
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This study investigates the evolution of mobile learning (M-Learning) applications in higher education between 2016 and 2023. This period marks an era of significant technological innovations and the profound impacts of the COVID-19 pandemic on education. The purpose of this research is to delineate how M-Learning applications are represented in the academic literature during this time and to identify research trends within this field. The research is based on a systematic review of 161 academic articles related to M-Learning, published between 2016 and 2023 in the Scopus and Web of Science databases. The study utilizes the TCCM (Theory, Context, Characteristics, Methodology) framework to conduct an in-depth analysis of theoretical approaches, research contexts, learning characteristics, and methodological strategies in the literature. The findings reveal that M-Learning positively impacts areas such as collaboration, skill development, and self-assessment among students. The effective use of mobile devices as educational tools by instructors and students is identified as crucial for the success of M-Learning applications. Moreover, the success of M-Learning is closely linked to users' attitudes toward technology and the integration of technological and pedagogical supports into the learning processes. This systematic review provides significant insights into how M-Learning can transform learning and teaching practices in higher education. It suggests strategic planning and further research for educators, policymakers, and researchers on integrating mobile technologies into learning processes. Specifically, there is a need to explore the long-term effects of M-Learning on student achievement and its applicability in various learning contexts.
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In physics learning, learning media are needed that can present visualizations of physics concepts and their applications so that they are easily understood by students. This study aims to: (1) develop learning media in the form of smartphone applications to facilitate students in learning physics on the topic of temperature and heat, (2) determine the quality of the media developed, (3) determine user responses to the media developed. This study uses a development research method with the ADDIE framework consisting of the stages of analyze, design, develop, implement, and evaluate. The application contains several menus including introduction, material, and quizzes. Based on expert assessment, the media is very suitable for use, the average assessment score is 3.48 (out of a maximum value of 4.00). In the implementation, the application was tested on high school students online. A sample of 51 students were involved in the implementation stage. Student responses during implementation were very positive so that the application developed has the potential to be used as an independent learning medium.
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This study looks into the impact of mobile coding apps on autonomous learning among tertiary students through a cross-sectional survey. Analyzing data from 377 students, primarily from the BS Information Technology program, the research reveals a potential gender gap in app usage and a reliance on smartphones for coding due to limited access to traditional computing devices. It highlights the widespread use of mobile coding applications, valued for their accessibility and hands-on experience, resulting in improved coding skills, critical thinking, and problem-solving abilities. While preferences leaned towards C++, a growing interest in Python was noted. These findings underscore the pivotal role of these apps in education, especially in resource-constrained environments, emphasizing the need for educational adaptation and policy changes to fully harness the transformative potential of technology in IT education, addressing disparities in access linked to income and gender while promoting autonomous learning.
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Instructional designers and educators recognize the potential of mobile technologies as a learning tool for students and have incorporated them into the distance learning environment. However, little research has been done to categorize the numerous examples of mobile learning in the context of distance education, and few instructional design guidelines based on a solid theoretical framework for mobile learning exist. In this paper I compare mobile learning (m-learning) with electronic learning (e-learning) and ubiquitous learning (u-learning) and describe the technological attributes and pedagogical affordances of mobile learning presented in previous studies. I modify transactional distance (TD) theory and adopt it as a relevant theoretical framework for mobile learning in distance education. Furthermore, I attempt to position previous studies into four types of mobile learning: 1) high transactional distance socialized m-learning, 2) high transactional distance individualized m-learning, 3) low transactional distance socialized m-learning, and 4) low transactional distance individualized m-learning. As a result, this paper can be used by instructional designers of open and distance learning to learn about the concepts of mobile learning and how mobile technologies can be incorporated into their teaching and learning more effectively.
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Along with advancing mobile technologies and proliferating mobile devices and applications, mobile learning research has gained great momentum in recent years. While there have been review articles summarizing past research, studies identifying mobile learning research priorities based on experts’ latest insights have been lacking. This study employed the Delphi method to obtain a consensus from experts about areas that are most in need of research in mobile learning. An international expert panel participated in a three-round Delphi process involving two cycles of online questionnaires and feedback reports. Participants responded to the question, “What should be the research priorities for the field of mobile learning over the next 5 years?” Ten research categories were identified and ranked in order of priority: 1) teaching and learning strategies; 2) affordances; 3) theory; 4) settings of learning; 5) evaluation/assessment; 6) learners; 7) mobile technologies and interface design; 8) context awareness and augmented reality; 9) infrastructure and management; and 10) country and digital divide. This study also reported expert-generated research statements for each research category and the importance of these research statements rated by the experts. Selected research papers were summarized to help contextualize the discussions of research categories and statements. Avec l'avancement des technologies mobiles et la prolifération des appareils mobiles et des applications, la recherche consacrée à l'apprentissage mobile a récemment pris de l’ampleur. Si des articles ont résumé les recherches antérieures, les études s’appuyant sur les dernières connaissances d'experts pour identifier les priorités de recherche sur l'apprentissage mobile font défaut. La présente étude a utilisé la méthode de Delphes pour obtenir un consensus des experts sur les domaines nécessitant le plus des recherches sur l'apprentissage mobile. Un groupe international d'experts a participé à un processus de Delphes structuré en trois rondes impliquant deux séries de questionnaires en ligne et des rapports de rétroaction. Les participants ont répondu à la question : "Quelles devraient être les priorités de recherche dans le domaine de l'apprentissage mobile pour les cinq prochaines années ?" Dix catégories de recherche ont été identifiées et classées par ordre de priorité : 1 ) stratégies d'enseignement et d'apprentissage ; 2 ) affordances ; 3 ) théorie ; 4 ) paramètres d’apprentissage ; 5 ) évaluation ; 6 ) apprenants ; 7 ) technologies mobiles et conception de l'interface ; 8 ) perception du contexte et réalité augmentée ; 9 ) infrastructure et gestion ; et 10 ) pays et fossé numérique. Cette étude a également repris les déclarations de recherche énoncées pour chaque catégorie par les experts ainsi que le classement par ordre d’importance des déclarations de recherche selon l’avis de ces experts. Quelques articles choisis ont été résumés pour faciliter la contextualisation des discussions portant sur les catégories de recherche et sur les déclarations.
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This study aims to identity the emerging research trends in the field of computed-supported collaborative learning (CSCL) so as to provide insights for researchers and educators into research topics and issues for further exploration. This paper analyzed the research topics, methods and technology adoptionof CSCL from 2003 to 2012. A total of 706 articles from 9 leading SSCI journals were selected and analyzed through the lens of research topics, research design, research methods, data sources, data analysis methods, research settings, research sample groups, research learning domain, types of collaborative learning, and technology for supporting collaborative learning. The results indicated that technologyin support of collaborative learning, interaction pattern and analysis, CSCL evaluation, and CSCL practice or applications in naturalistic teaching settings and community environment were the four major research topics. Qualitative research approach and descriptive research method were found in most of CSCL publications. Furthermore, it was found that the research topics significantly correlated with the research design and research methods. In the future, researchers and educators should pay more attention to technical affordance and facilitation in collaborative learning.
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This study aims to investigate (1) methods utilized in mobile computer-supported collaborative learning (mCSCL) research which focuses on studying, learning and collaboration mediated by mobile devices; (2) whether these methods have examined mCSCL effectively; (3) when the methods are administered; and (4) what methodological issues exist in mCSCL studies. It attempts to bring to light methods more conducive to examining the effectiveness of mCSCL and thus to sustain the practices. The research findings reveal a variety of methodological issues that need to be addressed. Comparison is made to the findings in CSCL research and other studies leveraged by mobile technologies. Potential ways to investigate the effectiveness of mCSCL practices are proposed.
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blockquote>Mobile handheld devices are increasingly being used in education. In this paper, we undertook a review of empirical based articles to summarise the current research regarding the use of mobile handheld devices (personal digital assistants/PDAs, palmtops, and mobile phones) in K-12 and higher education settings. This review was guided by the following four questions: (a) How are mobile handheld devices such as PDAs, palmtops, and mobile phones used by students and teachers? (b) What types of research methods have been applied using such devices? (c) What data collection methods are used in the research? and (d) What research topics have been conducted on these handheld devices in education settings, as well as their related findings? We summarise and discuss some major findings from the research, as well as several limitations of previous empirical studies. We conclude by providing some recommendations for future research related to mobile handheld devices in education settings. </p
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Abstract Mobile learning technology in the form of iPads has gained considerable attention recently in the literature on pedagogy and learning. This has led to a change in the roles of teachers and students, and the nature of the classroom interaction. What is not clear so far however, is how iPads have changed the nature of classroom talk and dialogic teaching. The present study aimed to examine the impact of iPad use on the opportunities for dialogic teaching in English for Academic Purposes (EAP) classes in an English medium university in the United Arab Emirates. The study reveals that although opportunities for dialogic teaching are both created and inhibited in classes utilizing the iPads, the most influential contributor to opportunities and restrictions lies depends on whether the teachers and students have adopted a dialogic stance. The study also revealed the need to examine dialogic teaching within the specific sociocultural and educational context of learning.